See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
  - cf2f502b56084a7f_train_data.json
  ds_type: json
  format: custom
  path: /root/G.O.D-test/core/data/cf2f502b56084a7f_train_data.json
  type:
    field_instruction: inputs
    field_output: targets
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_steps: 0
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: souging/c00a52fe-dcca-421a-95ee-beebcccae86b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000202
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 1
mlflow_experiment_name: /tmp/cf2f502b56084a7f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 0
saves_per_epoch: null
seed: 20
sequence_len: 1664
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
wandb_entity: null
wandb_mode: online
wandb_name: 3ae3daf1-db1e-411f-bf89-0b1501a12248
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3ae3daf1-db1e-411f-bf89-0b1501a12248
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null

c00a52fe-dcca-421a-95ee-beebcccae86b

This model is a fine-tuned version of jhflow/mistral7b-lora-multi-turn-v2 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.000202
• train_batch_size: 1
• eval_batch_size: 1
• seed: 20
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• total_eval_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• training_steps: 500

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.3